Communication systems generally provide information carrying signals on associated frequencies or wavelengths. Some portions of a bandwidth of interest may carry information signals (i.e. the occupied spectrum) and portions of a bandwidth of interest may not carry information signals (i.e. the available spectrum or “white spaces”). In such systems, it may be useful to identify the available spectrum to identify which frequencies or wavelengths could support additional information signals. Alternatively, it may be useful identify which portions of a bandwidth of interest are inappropriately occupied by information carrying signals, e.g. a portion of expected available spectrum is being used without authorization.
One example of a communication system wherein identifying available spectrum is useful is a cognitive radio system. Cognitive radio is a form of wireless communication that can automatically change its transmission and/or reception parameters to use the available spectrum for communication while avoiding the occupied spectrum. Cognitive radio thus optimizes the use of available spectrum while minimizing interference to users of the occupied spectrum.
Cognitive radio may be used in a variety of applications. For example, the Institute of Electrical and Electronics Engineers (IEEE) has established IEEE 802.22 as a standard for wireless regional area network (WRAN) using white spaces in the television (TV) frequency spectrum. The IEEE 802.22 WRAN standard is aimed at using cognitive radio to allow sharing of geographically available spectrum allocated to TV broadcast. IEEE 802.22 WRANs are designed to operate in the TV broadcast bands while assuring that no harmful interference is caused to the incumbent operation, e.g. in digital and analog TV broadcasting, and may be used, for example, to bring broadband access to hard-to-reach, low population density areas.
In systems configured to identify the occupied and/or available spectrum the technique used for spectrum sensing is an important aspect of the system. Several spectrum sensing techniques are known, each of which has advantages and disadvantages. These techniques range from low to high computation complexity and have various levels of performance in determining the presence of signals in noise.